In the field of imaging processing like in computer vision and in computational photography, it is desired to enhance the overall visual quality of a group of images. Such an enhancement can consist in improving the similarity between two images by performing a color calibration between two images that present color and possibly also content differences. Such a color calibration is also called color matching.
In other approaches, a color calibration is typically done by means of a histogram matching technique. The histogram matching technique consists in performing a color adjustment between two images presenting different exposures, in order to decrease the color discrepancy between the images. Histogram matching is known for example from “Digital Image Processing”, Gonzalez and Woods, N.J. Prentice Hall, 2006. Histogram matching is a technique for color matching, wherein the histogram of a source image is matched to the histogram of a reference image.
Nevertheless, in case the source image and the reference image are not perfectly aligned due to camera or object motion, histogram matching can generate artifacts due to mismatches. The histogram matching image, i.e. the outcome image generated by the histogram matching operation, may also present artifacts due to mismatches in case of large exposure differences between the source and reference images. Furthermore, histogram matching typically involves brightening dark regions of the source image, which at the same time increases the level of image noise and further decreases the quality of the resulting image.